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Affiliation of research teams building notable AI systems, by year of publication

See all data and research on:
Artificial Intelligence

What you should know about this indicator

  • The authors of the Epoch dataset have established a set of criteria to identify key AI systems, which they refer to as “notable”. To be considered notable, these systems must demonstrate the ability to learn, show tangible experimental results, and contribute advancements that push the boundaries of existing AI technology. The AI system must also have received extensive academic attention (evidenced by a high citation count), hold historical significance in the field, mark a substantial advancement in technology, or be implemented in a significant real-world context. The authors recognize the difficulty in evaluating the impact of newer AI systems since 2020 due to less data being available; because of this, they also employ subjective judgement in their selection process for recent developments.
  • Systems are classified as "Industry" when their authors have ties to private sector entities, "Academia" when the authors come from universities or scholarly institutions, and "Industry - Academia Collaboration" if a minimum of 30% of the authors represent each sector.
Affiliation of research teams building notable AI systems, by year of publication
Describes the sector (Industry, Academia, or Collaboration) where the authors of an AI system have their primary affiliations.
Source
Epoch (2024) – with minor processing by Our World in Data
Last updated
April 3, 2024
Next expected update
July 2024
Date range
1950–2024
Unit
AI systems

Sources and processing

This data is based on the following sources

Retrieved on
April 3, 2024
Retrieved from
https://epochai.org/mlinputs/visualization
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Epoch, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/mlinputs/visualization’ [online resource]

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

Processing involved calculating total number of AI systems developed within each category of reseacher affiliation for each year.

To streamline the categorization of researcher affiliations, the original data underwent the following transformations:

Consolidating Collaborations:

  • All variations of "Industry - Academia Collaboration" entries, regardless of their capitalization or leaning (towards academia or industry), were unified into a single "Collaboration" category.

Grouping Other Affiliations:

  • Affiliations explicitly labeled as "Research Collective" or "research collective", as well as those under "Government" and "Non-profit", were re-categorized under the "Other" label.

The aforementioned changes were implemented to make visualizations more coherent and concise.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Affiliation of research teams building notable AI systems, by year of publication”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch. Retrieved from http://staging-site-fix-deleted-assets-in-cache/grapher/affiliation-researchers-building-artificial-intelligence-systems-all [online resource]
How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Epoch (2024) – with minor processing by Our World in Data

Full citation

Epoch (2024) – with minor processing by Our World in Data. “Affiliation of research teams building notable AI systems, by year of publication” [dataset]. Epoch, “Parameter, Compute and Data Trends in Machine Learning” [original data]. Retrieved June 14, 2024 from http://staging-site-fix-deleted-assets-in-cache/grapher/affiliation-researchers-building-artificial-intelligence-systems-all